Application of Support Vector Machine Algorithm For Classification of Sleep Disorders

Sleep disorders such as insomnia and sleep apnea are health problems that can have a serious impact on a person's quality of life. Early detection of these disorders is important to prevent the risk of more severe complications. This study aims to build a sleep disorder classification model using the Support Vector Machine (SVM) algorithm by evaluating the influence of four types of kernels, namely Linear, Polynomial, Radial Basis Function (RBF), and Sigmoid. The dataset used comes from the Sleep Health and Lifestyle Dataset which contains information about individual characteristics related to sleep and lifestyle. The research process follows the CRISP-DM stages from data understanding, data preparation, modeling, to model evaluation using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the Polynomial kernel produces the best performance with 91.6% accuracy, followed by Linear, RBF, and Sigmoid. This finding shows that the selection of the right kernel in SVM has a significant effect on classification quality. This research contributes to the utilization of machine learning to detect sleep disorders and opens up opportunities for the development of more accurate and efficient diagnostic systems.